CS886 Winter09 - Schedule

This is a partial and tentative schedule only.  As the course progresses, the schedule will be adjusted.

Complementary Readings
Assigned Readings
Jan 7
Course overview

Jan 12
basics of probabilities and statistics

Jan 14
Bayesian networks, exact inference

Jan 19
lecture canceled

Jan 21
lecture canceled

Jan 26
approximate inference (Monte Carlo techniques)
GCSR Chapt 11, RN Sect 14.5, An Introduction to Monte Carlo Methods

An empirical analysis of likelihood-weighting simulation on a large, multiply connected medical belief network,

Loopy belief propagation for approximate inference: An empirical study

Jan 28
statistical learning (Bayesian learning, maximum likelihood, maximum a posteriori hypothesis)
RN Chapt 20
Feb 2
single parameter models, conjugate priors

A tutorial on learning with Bayesian networks
Feb 4
informative/non-informative priors

Feb 9
multi-parameter models

Bernardinelli, Clayton and Montomoli, Bayesian estimates of disease maps: how important are priors? Statis in Medicine 14, 2411--2431 (not available online, get it from the library)
Feb 11
hierarchical models

Feb 16
reading break

Feb 18
reading break

Feb 23
Mixture models, Bayesian clustering

Piotr Gmytrasiewicz and Prashant Doshi, "A Framework for Sequential Planning in Multiagent Settings", in Journal of AI Research (JAIR), Vol 24: 49-79, 2005
Feb 25
Dirichlet process (aka the Chinese restaurant process)

Mar 2
Hierarchical dirichlet process (aka the Chinese restaurant franchise)

Latent dirichlet allocation - stanford.edu [PDF] 
DM Blei, AY Ng, MI Jordan - The Journal of Machine Learning Research, 2003
Mar 4
Hierarchical dirichlet process (aka the Chinese restaurant franchise)

Mar 9
Pitman-Yor Process

Hierarchical Dirichlet Processes
Mar 11
Beta Process (aka the Indian buffet process)

Mar 16
Gaussian Process
Hierarchical Bayesian nonparametric models with applications. Y. W. Teh and M. I. Jordan. In N. Hjort, C. Holmes, P. Mueller, and S. Walker (Eds.), Bayesian Nonparametrics in Practice, Cambridge, UK: Cambridge University Press, to appear.
Mar 18
Regression with Gaussian Processes

Mar 23
Classification with Gaussian Processes
Y. Engel, P. Szabo, and D. Volkinshtein. Learning to control an octopus arm with Gaussian process temporal difference methods. In Yair Weiss, Bernhard Schölkopf, and John C. Platt, editors, Advances in Neural Information Processing Systems 18, pages 347-354, Cambridge, MA, U.S.A., 2006. The MIT Press.
Mar 25
Classification with Gaussian Processes

Mar 30
Covariance functions for Gaussian Processes
A. Kapoor, K. Grauman, R. Urtasun, and T. Darell. Active learning with Gaussian processes for object categorization. In Proceedings of the International Conference in Cmputer Vision, 2007.
Apr 1
Model selection for Gaussian Processes

Apr 6
Relation between Gaussian Processes and other approaches (e.g., support vector machines)
P. Sollich. Bayesian methods for support vector machines: Evidence and predictive class probabilities. Machine Learning, 46(1-3):21-52, 2002.
Apr 8
Relation between Gaussian Processes and other approaches (e.g., support vector machines)